# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Collect individual returns into a portfolio by assigning a weight to each stock

Choose your stocks.

from 2012-12-31 to 2017-12-31

1 Import stock prices

symbols <- c("MSFT", "AAPL", "GOOG") 

prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2012-12-31",
                 to = "2017-12-31")
prices
## # A tibble: 3,780 × 8
##    symbol date        open  high   low close   volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 MSFT   2012-12-31  26.6  26.8  26.4  26.7 42749500     21.6
##  2 MSFT   2013-01-02  27.2  27.7  27.1  27.6 52899300     22.3
##  3 MSFT   2013-01-03  27.6  27.6  27.2  27.2 48294400     22.0
##  4 MSFT   2013-01-04  27.3  27.3  26.7  26.7 52521100     21.6
##  5 MSFT   2013-01-07  26.8  26.9  26.6  26.7 37110400     21.5
##  6 MSFT   2013-01-08  26.8  26.8  26.5  26.5 44703100     21.4
##  7 MSFT   2013-01-09  26.7  26.8  26.6  26.7 49047900     21.5
##  8 MSFT   2013-01-10  26.6  27.0  26.3  26.5 71431300     21.3
##  9 MSFT   2013-01-11  26.5  26.9  26.3  26.8 55512100     21.6
## 10 MSFT   2013-01-14  26.9  27.1  26.8  26.9 48324400     21.7
## # ℹ 3,770 more rows

2 Convert prices to returns (quarterly)

asset_returns_tbl <- prices %>%
    group_by(symbol) %>%
    tq_transmute(select = adjusted,
                 mutate_fun = periodReturn,
                 period = "quarterly",
                 type = "log") %>%
    slice(-1) %>%
    ungroup() %>%
    set_names(c("asset", "date", "returns"))
asset_returns_tbl
## # A tibble: 60 × 3
##    asset date       returns
##    <chr> <date>       <dbl>
##  1 AAPL  2013-03-28 -0.178 
##  2 AAPL  2013-06-28 -0.103 
##  3 AAPL  2013-09-30  0.191 
##  4 AAPL  2013-12-31  0.169 
##  5 AAPL  2014-03-31 -0.0383
##  6 AAPL  2014-06-30  0.198 
##  7 AAPL  2014-09-30  0.0858
##  8 AAPL  2014-12-31  0.0956
##  9 AAPL  2015-03-31  0.124 
## 10 AAPL  2015-06-30  0.0122
## # ℹ 50 more rows

3 Assign a weight to each asset (change the weigting scheme)

symbols <- asset_returns_tbl %>%
    distinct(asset) %>% 
    pull()
weights <- c(0.3, 0.4, 0.3)
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL        0.3
## 2 GOOG        0.4
## 3 MSFT        0.3

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    tq_portfolio(assets_col = asset,
                 returns_col = returns,
                 weights = w_tbl,
                 rebalance_on = "quarters")
portfolio_returns_tbl
## # A tibble: 20 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-03-28           0.0159 
##  2 2013-06-28           0.0688 
##  3 2013-09-30           0.0462 
##  4 2013-12-31           0.187  
##  5 2014-03-31           0.0162 
##  6 2014-06-30           0.0795 
##  7 2014-09-30           0.0608 
##  8 2014-12-31          -0.00581
##  9 2015-03-31           0.0154 
## 10 2015-06-30           0.0108 
## 11 2015-09-30           0.0279 
## 12 2015-12-31           0.145  
## 13 2016-03-31           0.00545
## 14 2016-06-30          -0.0878 
## 15 2016-09-30           0.136  
## 16 2016-12-30           0.0308 
## 17 2017-03-31           0.114  
## 18 2017-06-30           0.0538 
## 19 2017-09-29           0.0680 
## 20 2017-12-29           0.107

5 Plot: Portfolio Histogram and Density

portfolio_returns_tbl %>%
    ggplot(mapping = aes(x = portfolio.returns)) +
    geom_histogram(fill = "maroon", binwidth = 0.0175) +
    geom_density() +
    # Formatting
    scale_x_continuous(labels = scales::percent_format()) +
    labs(x = "returns",
         y = "distribution",
         title = "Portfolio Histogram and Density")

What return should you expect from the portfolio in a typical quarter?

This portfolio can expect returns of about 2% in a typical quarter.